Can I train my bot with a new URL through the agent?

Yes! Use the `add_data_source` tool and provide the Bot ID and the target URL. The bot will automatically scrape and learn from that page.

How do I review the chat history of a specific user conversation?

First, use `list_conversations` to find the active sessions. Then, use `get_conversation_history` with the Conversation ID to retrieve the full log of messages.

Does the integration allow me to create a completely new bot?

Yes. Use the `create_bot` action and provide a name and a system prompt/instructions. The bot will be instantly created in your account.

Bot9 MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Bot9
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Bot9 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Bot9 "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Bot9?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Bot9 MCP Server

Connect your Bot9 account to any AI agent and orchestrate your customer support and conversational automation workflows through natural language.

Pydantic AI validates every Bot9 tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Bot Management — List all configured AI bots, retrieve specific configurations, and create new bots on the fly.
Training & Data Sources — List existing knowledge base sources and dynamically add new URLs for your bots to learn from.
Conversation Oversight — Retrieve active conversation lists and export historical chat logs for analysis.
Message Automation — Send messages to your bots programmatically to test responses or simulate user interactions.

The Bot9 MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Bot9 to Pydantic AI via MCP

Follow these steps to integrate the Bot9 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Bot9 with type-safe schemas

Why Use Pydantic AI with the Bot9 MCP Server

Pydantic AI provides unique advantages when paired with Bot9 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Bot9 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Bot9 connection logic from agent behavior for testable, maintainable code

Bot9 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Bot9 MCP Server delivers measurable value.

Type-safe data pipelines: query Bot9 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Bot9 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Bot9 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Bot9 responses and write comprehensive agent tests

Bot9 MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Bot9 to Pydantic AI via MCP:

add_data_source

Add a URL to train the bot

create_bot

Create a new AI chatbot

get_bot

Get details of a specific bot

get_conversation_history

Retrieve message history of a conversation

list_bots

List all AI bots

list_conversations

List active conversations for a bot

list_data_sources

List knowledge base sources for a bot

send_message

Send a message to a bot and get a response

Example Prompts for Bot9 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Bot9 immediately.

"List all bots in my Bot9 account."

"Add the URL https://example.com/pricing to bot_123's knowledge base."

"Get the chat history for conversation conv_789 on bot_123."

Troubleshooting Bot9 MCP Server with Pydantic AI

Common issues when connecting Bot9 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Bot9 + Pydantic AI FAQ

Common questions about integrating Bot9 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Bot9 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.